List Index out of range gemini

[Irfan123fw]

Checked other resources

• [X] I added a very descriptive title to this issue.
• [X] I searched the LangChain documentation with the integrated search.
• [X] I used the GitHub search to find a similar question and didn't find it.
• [X] I am sure that this is a bug in LangChain rather than my code.

Example Code

llm = GoogleGenerativeAI(

  model="gemini-pro",   temperature=0.3,   max_output_tokens=2048,   )   chain = ConversationalRetrievalChain.from_llm(   llm=llm,   chain_type="stuff",   retriever= st.session_state.compression_retriever_reordered,   verbose=True,   combine_docs_chain_kwargs={"prompt": st.session_state.prompt},   return_source_documents=True,   )   conversation = get_conversation_string(st.session_state.messages)       res = chain({"question":user_question,"chat_history":chat_history})   answer =res["answer"]

Error Message and Stack Trace (if applicable)

IndexError: list index out of range Traceback: File "/home/user/.local/lib/python3.10/site-packages/streamlit/runtime/scriptrunner/script_runner.py", line 534, in _run_script exec(code, module.dict) File "/home/user/app/app.py", line 210, in res = chain({"question":user_question,"chat_history":chat_history}) File "/home/user/.local/lib/python3.10/site-packages/langchain_core/_api/deprecation.py", line 145, in warning_emitting_wrapper return wrapped(*args, kwargs) File "/home/user/.local/lib/python3.10/site-packages/langchain/chains/base.py", line 383, in call return self.invoke( File "/home/user/.local/lib/python3.10/site-packages/langchain/chains/base.py", line 168, in invoke raise e File "/home/user/.local/lib/python3.10/site-packages/langchain/chains/base.py", line 158, in invoke self._call(inputs, run_manager=run_manager) File "/home/user/.local/lib/python3.10/site-packages/langchain/chains/conversational_retrieval/base.py", line 166, in _call answer = self.combine_docs_chain.run( File "/home/user/.local/lib/python3.10/site-packages/langchain_core/_api/deprecation.py", line 145, in warning_emitting_wrapper return wrapped(*args, *kwargs) File "/home/user/.local/lib/python3.10/site-packages/langchain/chains/base.py", line 555, in run return self(kwargs, callbacks=callbacks, tags=tags, metadata=metadata)[ File "/home/user/.local/lib/python3.10/site-packages/langchain_core/_api/deprecation.py", line 145, in warning_emitting_wrapper return wrapped(args, kwargs) File "/home/user/.local/lib/python3.10/site-packages/langchain/chains/base.py", line 383, in call return self.invoke( File "/home/user/.local/lib/python3.10/site-packages/langchain/chains/base.py", line 168, in invoke raise e File "/home/user/.local/lib/python3.10/site-packages/langchain/chains/base.py", line 158, in invoke self._call(inputs, run_manager=run_manager) File "/home/user/.local/lib/python3.10/site-packages/langchain/chains/combine_documents/base.py", line 136, in _call output, extra_return_dict = self.combine_docs( File "/home/user/.local/lib/python3.10/site-packages/langchain/chains/combine_documents/stuff.py", line 244, in combine_docs return self.llm_chain.predict(callbacks=callbacks, *inputs), {} File "/home/user/.local/lib/python3.10/site-packages/langchain/chains/llm.py", line 293, in predict return self(kwargs, callbacks=callbacks)[self.output_key] File "/home/user/.local/lib/python3.10/site-packages/langchain_core/_api/deprecation.py", line 145, in warning_emitting_wrapper return wrapped(args, **kwargs) File "/home/user/.local/lib/python3.10/site-packages/langchain/chains/base.py", line 383, in call return self.invoke( File "/home/user/.local/lib/python3.10/site-packages/langchain/chains/base.py", line 168, in invoke raise e File "/home/user/.local/lib/python3.10/site-packages/langchain/chains/base.py", line 158, in invoke self._call(inputs, run_manager=run_manager) File "/home/user/.local/lib/python3.10/site-packages/langchain/chains/llm.py", line 104, in _call return self.create_outputs(response)[0] File "/home/user/.local/lib/python3.10/site-packages/langchain/chains/llm.py", line 258, in create_outputs result = [ File "/home/user/.local/lib/python3.10/site-packages/langchain/chains/llm.py", line 261, in self.output_key: self.output_parser.parse_result(generation), File "/home/user/.local/lib/python3.10/site-packages/langchain_core/output_parsers/base.py", line 219, in parse_result return self.parse(result[0].text)

Description

im trying to use gemini pro, but got error list index out of range

System Info

python = 3.11 langchain-google-gen-ai = 0.0.9 langchain = 0.1.5

[dosubot[bot]]

🤖

Hey @Irfan123fw! Great to see you back here. Hope you're doing well!

Thank you for providing the detailed error report. The error you're encountering, IndexError: list index out of range, typically occurs when you're trying to access an index that doesn't exist in the list. In your case, it seems to be happening in the parse_result method of the OutputParser class in the langchain_core package.

Based on the stack trace you provided, it seems that the parse_result method is trying to access the first element of the result list (result[0].text), but the result list is empty. This could be due to the GoogleGenerativeAI model not returning any results, or the results not being processed correctly.

I found a similar issue in the LangChain repository: Issue: when using the map_rerank & refine, occur the " list index out of range" (already modify the llm.py file, but only map_reduce can work well). The solution provided in this issue was to modify the _process_results method to raise a ValueError with a descriptive message if sorted_res is empty. This could help you in debugging the issue.

Here is the suggested modification:

def _process_results(
        self,
        docs: List[Document],
        results: Sequence[Union[str, List[str], Dict[str, str]]],
    ) -> Tuple[str, dict]:
        typed_results = cast(List[dict], results)
        sorted_res = sorted(
            zip(typed_results, docs), key=lambda x: -int(x[0][self.rank_key])
        )
        if not sorted_res:
            raise ValueError("No results to process.")
        output, document = sorted_res[0]
        extra_info = {}
        if self.metadata_keys is not None:
            for key in self.metadata_keys:
                extra_info[key] = document.metadata[key]
        if self.return_intermediate_steps:
            extra_info["intermediate_steps"] = results
        return output[self.answer_key], extra_info

However, please note that this is a workaround and not a definitive solution. The root cause of the issue seems to be that the GoogleGenerativeAI model is not returning any results. You might want to check the inputs you're providing to the model and make sure they're correct.

Also, I noticed that you're using LangChain version 0.1.5, but the latest version is 0.1.8. It might be worth updating to the latest version to see if the issue persists.

I hope this helps! If you have any further questions or if the issue persists, please provide more details about the inputs you're providing to the GoogleGenerativeAI model and the ConversationalRetrievalChain, and I'll be happy to assist you further.

Sources

• Issue: when using the map_rerank & refine, occur the " list index out of range" (already modify the llm.py file, but only map_reduce can work well)
• libs/partners/google-genai/langchain_google_genai/chat_models.py
• libs/langchain/langchain/chains/conversational_retrieval/base.py

#### About Dosu

This response is meant to be useful and save you time. It is **not meant to be a precise solution**, but rather a starting point for your own research. Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant. If you want to continue the conversation, start your reply with @dosu-bot.

[Irfan123fw]

@dosu-bot. how to solve this error? i want to model returning any results, or the results being processed correctly. can you help me

[dosubot[bot]]

🤖

Based on the error you're encountering, it seems that the result list is empty when the parse_result method tries to access its first element. This is causing the IndexError: list index out of range error.

To resolve this, you can add a check at the beginning of the parse_result method to ensure that the result list is not empty before trying to access its first element. If the result list is empty, the method can return a default value or raise an exception. Here's how you can modify the parse_result method:

def parse_result(self, result: List[Generation], *, partial: bool = False) -> T:
    if not result:
        raise ValueError("The result list is empty.")
    return self.parse(result[0].text)

In this modification, if the result list is empty, the method raises a ValueError with a message indicating that the result list is empty. You can replace this exception with any other appropriate action based on your application's requirements.

Please note that this modification should be done in the OutputParser class in the langchain_core package. You can find the source code for this class in the libs/core/langchain_core/output_parsers/base.py file in the LangChain repository.

I hope this helps! If you have any other questions or need further clarification, feel free to ask.

Source

Sources

• libs/core/langchain_core/output_parsers/base.py

#### About Dosu

This response is meant to be useful and save you time. It is **not meant to be a precise solution**, but rather a starting point for your own research. Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant. If you want to continue the conversation, start your reply with @dosu-bot.

[Irfan123fw]

i ant to hosted at hf spaces, so i cant modif llms.py @dosu-bot.

[dosubot[bot]]

🤖

It sounds like you're looking to host your project on HF Spaces and you're having trouble modifying the llms.py file. I'd be happy to help you with that. Could you provide more details about the specific modifications you're trying to make?

#### About Dosu

This response is meant to be useful and save you time. It is **not meant to be a precise solution**, but rather a starting point for your own research. Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant. If you want to continue the conversation, start your reply with @dosu-bot.

[alphacorpai]

Did you find any solutions?

I am encountering the same error: "list index out of range" when calling the Gemini API directly from Google. Everything functions correctly if the prompt I provide requires a short response. However, if I submit a prompt that demands a longer text output, it exceeds the maximum token limit, resulting in the "list index out of range" error.

Other APIs simply return incomplete text. This bug is a headache for me at the moment, and I haven't been able to find a solution.

[Irfan123fw]

I didn't get the solution, I just switched to using chatvertex ai, and it looks better than googleg gen ai, I didn't find error list index out of range anymore

[aditya-shirodkar]

I have the same issue, dropping the max_output_tokens variable seems to fix it. Could this be a bug?

[duob-ai]

I have the same issue, dropping the max_output_tokens variable seems to fix it. Could this be a bug?

What value did you use for max_output_tokens?